Pre-training.
We pre-train ConvMesh following a class conditional setting for 600 epochs, with a batch size of 128, which takes 15 hours on four Nvidia V100 GPUs.
The generator is updated once every three iterations with a learning rate of \(1\times 10^{-4}\)  whereas the discriminators are updated concurrently twice every three iterations with a learning rate of \(4\times 10^{-4}\) . We use the Adam optimizer [1]} with \(\beta _1=0\)  and \(\beta _2=0.9\) . For the objective function \(\mathcal {L}_{G}\)  in the main paper, we let \(\lambda _{uv}=1\)  and \(\lambda _{I}=0.04\) . We use the same settings for CUB and PASCAL 3D+.
